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05/21/2019
Explainable Machine Learning for Scientific Insights and Discoveries
Machine learning methods have been remarkably successful for a wide rang...
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03/25/2019
Few-Shot Learning-Based Human Activity Recognition
Few-shot learning is a technique to learn a model with a very small amou...
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11/29/2018
Binary Sequence Set Design for Interferer Rejection in Multi-Branch Modulation
Wideband communication is often expected to deal with a very wide spectr...
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11/14/2018
Design of Spectrally Shaped Binary Sequences via Randomized Convex Relaxation
Wideband communication receivers often deal with the problems of detecti...
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08/05/2018
Autoencoder Based Sample Selection for Self-Taught Learning
Self-taught learning is a technique that uses a large number of unlabele...
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01/07/2018
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Feature selection is a dimensionality reduction technique that selects a...
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01/03/2017
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation
This paper proposes a new hyperspectral unmixing method for nonlinearly ...
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06/27/2016
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series
This paper proposes an out-of-sample extension framework for a global ma...
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06/15/2016
Masking Strategies for Image Manifolds
We consider the problem of selecting an optimal mask for an image manifo...
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02/11/2016
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Hyperspectral signature classification is a quantitative analysis approa...
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01/07/2009